{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SBI with iid data and permutation-invariant embeddings\n",
    "\n",
    "There are scenarios in which we observe multiple data points per experiment and we can assume that they are independent and identically distributed (iid, i.e., they are assumed to have the same underlying model parameters). \n",
    "For example, in decision-making experiments, the experiment is often repeated in trials with the same experimental settings and conditions. The corresponding set of trials is then assumed to be \"iid\" given a single parameter set. \n",
    "In such a scenario, we may want to obtain the posterior given a set of observation $p(\\theta | X=\\{x_i\\}_i^N)$. \n",
    "\n",
    "### Amortization of neural network training: iid-inference with NLE / NRE\n",
    "For some SBI variants the iid assumption can be exploited: when using a likelihood-based SBI method (`SNLE`, `SNRE`) one can train the density or ratio estimator on single-trial data, and then perform inference with `MCMC` or variational inference (`VI`). Crucially, because the data is iid and the estimator is trained on single-trial data, one can repeat the inference with a different `x_o` (a different set of trials, or different number of trials) without having to retrain the density estimator. One can interpet this as amortization of the SBI training: we can obtain a neural likelihood, or likelihood-ratio estimate for new `x_o`s without retraining, but we still have to run `MCMC` or `VI` to do inference. \n",
    "\n",
    "In addition, one cannot only change the number of trials of a new `x_o`, but also the entire inference setting. \n",
    "For example, one can apply hierarchical inference with changing hierarchical denpendencies between the model parameters--all without having to retrain the density estimator because it estimates single-trail likelihoods.\n",
    "\n",
    "### Full amortization: iid-inference with NPE and permutation-invariant embedding nets\n",
    "When performing neural posterior estimation (`SNPE`) we cannot exploit the iid assumption directly. \n",
    "Thus, the underlying neural network takes `x` as input and predicts the parameters of the density estimator. \n",
    "As a consequence, if `x` is a set of iid observations $X=\\{x_i\\}_i^N$ then the neural network has to be invariant to permutations of this set, i.e., it has to be permutation invariant. In addition, the neural network has to be able to consume a varying number of iid datapoints in order to be amortized over the number of trials.\n",
    "Therefore, in order to use `SNPE` for inference on iid data, we need to provide a corresponding embedding network that handles the iid-data. \n",
    "This will likely require some hyperparameter tuning and more training data for inference to work accurately. But once we have this, inference is fully amortized, i.e., we can get new posterior samples almost instantly without retraining and without running `MCMC` or `VI`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SBI with trial-based data\n",
    "\n",
    "For illustration, we use a simple linear Gaussian simulator, as in previous tutorials. The simulator takes a single parameter (vector) which is the mean of a Gaussian. The simulator then adds noise with a fixed variance (set to one). \n",
    "We define a Gaussian prior over the mean and perform inference. \n",
    "\n",
    "The observed data is also sampled from a Gaussian with some fixed \"ground-truth\" parameter $\\theta_o$. \n",
    "Crucially, the observed data `x_o` can consist of multiple samples given the same ground-truth parameters and these samples are iid given $\\theta$: \n",
    "\n",
    "$$ \n",
    "\\theta \\sim \\mathcal{N}(\\mu_0,\\; \\Sigma_0) \\\\\n",
    "x | \\theta \\sim \\mathcal{N}(\\theta,\\; \\Sigma=I) \\\\\n",
    "\\mathbf{x_o} = \\{x_o^i\\}_{i=1}^N \\sim  \\mathcal{N}(\\theta_o,\\; \\Sigma=I)\n",
    "$$\n",
    "\n",
    "For this toy problem, the ground-truth posterior is well defined, it is again a Gaussian, centered on the mean of $\\mathbf{x_o}$ and with variance scaled by the number of trials $N$, i.e., the more trials we observe, the more information about the underlying $\\theta_o$ we have and the more concentrated the posteriors becomes.\n",
    "\n",
    "We will illustrate this below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from torch import zeros, ones, eye\n",
    "from torch.distributions import MultivariateNormal\n",
    "from sbi.inference import SNLE, SNPE, prepare_for_sbi, simulate_for_sbi\n",
    "from sbi.analysis import pairplot\n",
    "from sbi.utils.metrics import c2st\n",
    "\n",
    "from sbi.simulators.linear_gaussian import (\n",
    "    linear_gaussian,\n",
    "    true_posterior_linear_gaussian_mvn_prior,\n",
    ")\n",
    "\n",
    "# Seeding\n",
    "torch.manual_seed(1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gaussian simulator\n",
    "theta_dim = 2\n",
    "x_dim = theta_dim\n",
    "\n",
    "# likelihood_mean will be likelihood_shift+theta\n",
    "likelihood_shift = -1.0 * zeros(x_dim)\n",
    "likelihood_cov = 0.3 * eye(x_dim)\n",
    "\n",
    "prior_mean = zeros(theta_dim)\n",
    "prior_cov = eye(theta_dim)\n",
    "prior = MultivariateNormal(loc=prior_mean, covariance_matrix=prior_cov)\n",
    "\n",
    "# Define Gaussian simulator\n",
    "simulator, prior = prepare_for_sbi(\n",
    "    lambda theta: linear_gaussian(theta, likelihood_shift, likelihood_cov), prior\n",
    ")\n",
    "\n",
    "\n",
    "# Use built-in function to obtain ground-truth posterior given x_o\n",
    "def get_true_posterior_samples(x_o, num_samples=1):\n",
    "    return true_posterior_linear_gaussian_mvn_prior(\n",
    "        x_o, likelihood_shift, likelihood_cov, prior_mean, prior_cov\n",
    "    ).sample((num_samples,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The analytical posterior concentrates around true parameters with increasing number of IID trials "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_trials = [1, 5, 15, 20]\n",
    "theta_o = zeros(1, theta_dim)\n",
    "\n",
    "# Generate multiple x_os with increasing number of trials.\n",
    "xos = [theta_o.repeat(nt, 1) for nt in num_trials]\n",
    "\n",
    "# Obtain analytical posterior samples for each of them.\n",
    "true_samples = [get_true_posterior_samples(xo, 5000) for xo in xos]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot them in one pairplot as contours (obtained via KDE on the samples).\n",
    "fig, ax = pairplot(\n",
    "    true_samples,\n",
    "    points=theta_o,\n",
    "    diag=\"kde\",\n",
    "    upper=\"contour\",\n",
    "    kde_offdiag=dict(bins=50),\n",
    "    kde_diag=dict(bins=100),\n",
    "    contour_offdiag=dict(levels=[0.95]),\n",
    "    points_colors=[\"k\"],\n",
    "    points_offdiag=dict(marker=\"*\", markersize=10),\n",
    ")\n",
    "plt.sca(ax[1, 1])\n",
    "plt.legend(\n",
    "    [f\"{nt} trials\" if nt > 1 else f\"{nt} trial\" for nt in num_trials]\n",
    "    + [r\"$\\theta_o$\"],\n",
    "    frameon=False,\n",
    "    fontsize=12,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, with increasing number of trials the posterior density concentrates around the true underlying parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IID inference with NLE\n",
    "\n",
    "(S)NLE and (S)NRE can perform inference given multiple IID obserations by using only single-trial training data (i.e., for training, we run the simulator only once per parameter set). Once the likelihood is learned on single trials (i.e., a neural network that predicts the likelihood of a single observation given a parameter set), one can sample the posterior for any number of trials. This works because, given a single-trial neural likelihood from (S)NLE or (S)NRE, we can calculate the joint likelihoods of all trials by multiplying them together (or adding them in log-space). The joint likelihood can then be plugged into `MCMC` or `VI`. `sbi` takes care of all of these steps, so you do not have to implement anything yourself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c4eac9f56954a598631db3ed5ad5b20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running 10000 simulations.:   0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Neural network successfully converged after 43 epochs."
     ]
    }
   ],
   "source": [
    "# Train SNLE.\n",
    "inferer = SNLE(prior, show_progress_bars=True, density_estimator=\"mdn\")\n",
    "theta, x = simulate_for_sbi(simulator, prior, 10000, simulation_batch_size=1000)\n",
    "inferer.append_simulations(theta, x).train(training_batch_size=1000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "708831283b0547569d015fcfb9c7fd90",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running vectorized MCMC with 50 chains:   0%|          | 0/75000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 5 was passed. It will be interpreted as a batch of independent and identically\n",
      "            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n",
      "            same underlying (unknown) parameter. The resulting posterior will be with\n",
      "            respect to entire batch, i.e,. p(theta | X).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2e3634f9fac04af3b49d3263464f9a6c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running vectorized MCMC with 50 chains:   0%|          | 0/75000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 15 was passed. It will be interpreted as a batch of independent and identically\n",
      "            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n",
      "            same underlying (unknown) parameter. The resulting posterior will be with\n",
      "            respect to entire batch, i.e,. p(theta | X).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b31aef511ea84ab7b657651ac078127a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running vectorized MCMC with 50 chains:   0%|          | 0/75000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/janbolts/qode/sbi/sbi/utils/sbiutils.py:342: UserWarning: An x with a batch size of 20 was passed. It will be interpreted as a batch of independent and identically\n",
      "            distributed data X={x_1, ..., x_n}, i.e., data generated based on the\n",
      "            same underlying (unknown) parameter. The resulting posterior will be with\n",
      "            respect to entire batch, i.e,. p(theta | X).\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5452859656d9465fa32d4fb56253e386",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running vectorized MCMC with 50 chains:   0%|          | 0/75000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Obtain posterior samples for different number of iid xos.\n",
    "nle_samples = []\n",
    "num_samples = 5000\n",
    "\n",
    "mcmc_parameters = dict(\n",
    "    num_chains=50,\n",
    "    thin=10,\n",
    "    warmup_steps=50,\n",
    "    init_strategy=\"proposal\",\n",
    ")\n",
    "mcmc_method = \"slice_np_vectorized\"\n",
    "\n",
    "posterior = inferer.build_posterior(\n",
    "    mcmc_method=mcmc_method,\n",
    "    mcmc_parameters=mcmc_parameters,\n",
    ")\n",
    "\n",
    "# Generate samples with MCMC given the same set of x_os as above.\n",
    "for xo in xos:\n",
    "    nle_samples.append(posterior.sample(sample_shape=(num_samples,), x=xo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `sbi` warns about `iid-x` with increasing number of trial here. We ignore the warning because that's exactly what we want to do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot them in one pairplot as contours (obtained via KDE on the samples).\n",
    "fig, ax = pairplot(\n",
    "    nle_samples,\n",
    "    points=theta_o,\n",
    "    diag=\"kde\",\n",
    "    upper=\"contour\",\n",
    "    kde_offdiag=dict(bins=50),\n",
    "    kde_diag=dict(bins=100),\n",
    "    contour_offdiag=dict(levels=[0.95]),\n",
    "    points_colors=[\"k\"],\n",
    "    points_offdiag=dict(marker=\"*\", markersize=10),\n",
    ")\n",
    "plt.sca(ax[1, 1])\n",
    "plt.legend(\n",
    "    [f\"{nt} trials\" if nt > 1 else f\"{nt} trial\" for nt in num_trials]\n",
    "    + [r\"$\\theta_o$\"],\n",
    "    frameon=False,\n",
    "    fontsize=12,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pairplot above already indicates that (S)NLE is well able to obtain accurate posterior samples also for increasing number of trials (note that we trained the single-round version of SNLE so that we did not have to re-train it for new $x_o$). \n",
    "\n",
    "Quantitatively we can measure the accuracy of SNLE by calculating the `c2st` score between SNLE and the true posterior samples, where the best accuracy is perfect for `0.5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c2st score for num_trials=1: 0.50\n",
      "c2st score for num_trials=5: 0.51\n",
      "c2st score for num_trials=15: 0.51\n",
      "c2st score for num_trials=20: 0.51\n"
     ]
    }
   ],
   "source": [
    "cs = [\n",
    "    c2st(torch.from_numpy(s1), torch.from_numpy(s2))\n",
    "    for s1, s2 in zip(true_samples, nle_samples)\n",
    "]\n",
    "\n",
    "for _ in range(len(num_trials)):\n",
    "    print(f\"c2st score for num_trials={num_trials[_]}: {cs[_].item():.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IID inference with NPE using permutation-invariant embedding nets\n",
    "\n",
    "For NPE we need to define an embedding net that handles the set-like structure of iid-data, i.e., that it permutation invariant and can handle different number of trials.  \n",
    "\n",
    "We implemented several embedding net classes that allow to construct such a permutation- and number-of-trials invariant embedding net. \n",
    "\n",
    "To become permutation invariant, the neural net first learns embeddings for single trials and then performs a permutation invariant operation on those embeddings, e.g., by taking the sum or the mean (Chen et al. 2018, Radev et al. 2021).\n",
    "\n",
    "To become invariant w.r.t. the number-of-trials, we train the net with varying number of trials for each parameter setting. This means that, unlike for (S)NLE and (S)NRE, (S)NPE requires to run the simulator multiple times for individual parameter sets to generate the training data.\n",
    "\n",
    "In order to implement this in `sbi`, \"unobserved\" trials in the training dataset have to be masked by NaNs (and ignore the resulting SBI warning about NaNs in the training data).\n",
    "\n",
    "### Construct training data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we need to fix the maximum number of trials.\n",
    "max_num_trials = 20\n",
    "\n",
    "# construct training data set: we want to cover the full range of possible number of\n",
    "# trials\n",
    "num_training_samples = 5000\n",
    "theta = prior.sample((num_training_samples,))\n",
    "\n",
    "# there are certainly smarter ways to construct the training data set, but we go with a\n",
    "# for loop here for illustration purposes.\n",
    "x = torch.ones(num_training_samples * max_num_trials, max_num_trials, x_dim) * float(\n",
    "    \"nan\"\n",
    ")\n",
    "for i in range(num_training_samples):\n",
    "    xi = simulator(theta[i].repeat(max_num_trials, 1))\n",
    "    for j in range(max_num_trials):\n",
    "        x[i * max_num_trials + j, : j + 1, :] = xi[: j + 1, :]\n",
    "\n",
    "theta = theta.repeat_interleave(max_num_trials, dim=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build embedding net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sbi.neural_nets.embedding_nets import (\n",
    "    FCEmbedding,\n",
    "    PermutationInvariantEmbedding,\n",
    ")\n",
    "from sbi.utils import posterior_nn\n",
    "\n",
    "# embedding\n",
    "latent_dim = 10\n",
    "single_trial_net = FCEmbedding(\n",
    "    input_dim=theta_dim,\n",
    "    num_hiddens=40,\n",
    "    num_layers=2,\n",
    "    output_dim=latent_dim,\n",
    ")\n",
    "embedding_net = PermutationInvariantEmbedding(\n",
    "    single_trial_net,\n",
    "    trial_net_output_dim=latent_dim,\n",
    "    # NOTE: post-embedding is not needed really.\n",
    "    num_layers=1,\n",
    "    num_hiddens=10,\n",
    "    output_dim=10,\n",
    ")\n",
    "\n",
    "# we choose a simple MDN as the density estimator.\n",
    "# NOTE: we turn off z-scoring of the data, as we used NaNs for the missing trials.\n",
    "density_estimator = posterior_nn(\"mdn\", embedding_net=embedding_net, z_score_x=\"none\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Found 95000 NaN simulations and 0 Inf simulations. They are not excluded from training due to `exclude_invalid_x=False`.Training will likely fail, we strongly recommend `exclude_invalid_x=True` for Single-round NPE.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Neural network successfully converged after 168 epochs."
     ]
    }
   ],
   "source": [
    "inference = SNPE(prior, density_estimator=density_estimator)\n",
    "# NOTE: we don't exclude invalid x because we used NaNs for the missing trials.\n",
    "inference.append_simulations(\n",
    "    theta,\n",
    "    x,\n",
    "    exclude_invalid_x=False,\n",
    ").train(training_batch_size=1000)\n",
    "posterior = inference.build_posterior()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Amortized inference\n",
    "Comparing runtimes, we see that the NPE training takes a bit longer than the training on single trials for `NLE` above. \n",
    "\n",
    "However, we trained the density estimator such that it can handle multiple and changing number of iid trials (up to 20). \n",
    "\n",
    "Thus, we can obtain posterior samples for different `x_o` with just a single forward pass instead of having to run `MCMC` for each new observation.\n",
    "\n",
    "As you can see below, the c2st score for increasing number of observed trials remains close to the ideal `0.5`. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b2946f18f634a1f8670b59d01a35c8e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39b65d79766c47049c9179239e58e4ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9bbdf2b121241c0b8b62d033f481800",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2472b611e7634ad2aa4382807501fd7a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c2st score for num_trials=1: 0.50\n",
      "c2st score for num_trials=5: 0.50\n",
      "c2st score for num_trials=15: 0.52\n",
      "c2st score for num_trials=20: 0.55\n"
     ]
    }
   ],
   "source": [
    "npe_samples = []\n",
    "for xo in xos:\n",
    "    # we need to pad the x_os with NaNs to match the shape of the training data.\n",
    "    xoi = torch.ones(1, max_num_trials, x_dim) * float(\"nan\")\n",
    "    xoi[0, : len(xo), :] = xo\n",
    "    npe_samples.append(posterior.sample(sample_shape=(num_samples,), x=xoi))\n",
    "\n",
    "cs = [c2st(torch.from_numpy(s1), s2) for s1, s2 in zip(true_samples, npe_samples)]\n",
    "\n",
    "for _ in range(len(num_trials)):\n",
    "    print(f\"c2st score for num_trials={num_trials[_]}: {cs[_].item():.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "569de46ccc734c55b709e205868a5330",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c84c3704f9d14e089bdb8f6427db619f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e1a192da69e4b1986b0cdb9ee0c6ba5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40b1854b403b455588a912105f3275c1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_trials = [1, 5, 15, 20]\n",
    "xos = [theta_o.repeat(nt, 1) for nt in num_trials]\n",
    "\n",
    "npe_samples = []\n",
    "for xo in xos:\n",
    "    # we need to pad the x_os with NaNs to match the shape of the training data.\n",
    "    xoi = torch.ones(1, max_num_trials, x_dim) * float(\"nan\")\n",
    "    xoi[0, : len(xo), :] = xo\n",
    "    npe_samples.append(posterior.sample(sample_shape=(num_samples,), x=xoi))\n",
    "\n",
    "\n",
    "# Plot them in one pairplot as contours (obtained via KDE on the samples).\n",
    "fig, ax = pairplot(\n",
    "    npe_samples,\n",
    "    points=theta_o,\n",
    "    diag=\"kde\",\n",
    "    upper=\"contour\",\n",
    "    kde_offdiag=dict(bins=50),\n",
    "    kde_diag=dict(bins=100),\n",
    "    contour_offdiag=dict(levels=[0.95]),\n",
    "    points_colors=[\"k\"],\n",
    "    points_offdiag=dict(marker=\"*\", markersize=10),\n",
    ")\n",
    "plt.sca(ax[1, 1])\n",
    "plt.legend(\n",
    "    [f\"{nt} trials\" if nt > 1 else f\"{nt} trial\" for nt in num_trials]\n",
    "    + [r\"$\\theta_o$\"],\n",
    "    frameon=False,\n",
    "    fontsize=12,\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6f02b27bd2049cd92e81f12653a294c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d29ee21beb747c1acd180b786376a54",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "73f38c8f8e074282bac41e46d61340a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "904285d6dee54afda03a537758f41a51",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can easily obtain posteriors for many different x_os, instantly, because NPE is fully amortized:\n",
    "num_trials = [2, 4, 6, 8, 12, 14, 18]\n",
    "npe_samples = []\n",
    "for xo in xos:\n",
    "    # we need to pad the x_os with NaNs to match the shape of the training data.\n",
    "    xoi = torch.ones(1, max_num_trials, x_dim) * float(\"nan\")\n",
    "    xoi[0, : len(xo), :] = xo\n",
    "    npe_samples.append(posterior.sample(sample_shape=(num_samples,), x=xoi))\n",
    "\n",
    "\n",
    "# Plot them in one pairplot as contours (obtained via KDE on the samples).\n",
    "fig, ax = pairplot(\n",
    "    npe_samples,\n",
    "    points=theta_o,\n",
    "    diag=\"kde\",\n",
    "    upper=\"contour\",\n",
    "    kde_offdiag=dict(bins=50),\n",
    "    kde_diag=dict(bins=100),\n",
    "    contour_offdiag=dict(levels=[0.95]),\n",
    "    points_colors=[\"k\"],\n",
    "    points_offdiag=dict(marker=\"*\", markersize=10),\n",
    ")\n",
    "plt.sca(ax[1, 1])\n",
    "plt.legend(\n",
    "    [f\"{nt} trials\" if nt > 1 else f\"{nt} trial\" for nt in num_trials]\n",
    "    + [r\"$\\theta_o$\"],\n",
    "    frameon=False,\n",
    "    fontsize=12,\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.17"
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  "toc": {
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {},
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   "toc_window_display": false
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  "vscode": {
   "interpreter": {
    "hash": "9ef9b53a5ce850816b9705a866e49207a37a04a71269aa157d9f9ab944ea42bf"
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